构建首个面向RNA三维结构功能预测的标准化基准数据集
A Comprehensive Benchmark for RNA 3D Structure-Function Modeling
- 设计7个专用数据集，支持深度学习在RNA结构功能预测中的应用
- 基于rnaglib框架实现数据封装与评估工具，提升可复现性
- 适合从事核酸结构建模、生物信息学研究者使用
RNA结构与功能的关系近年来受到深度学习领域的关注，随着核酸结构模型的发展，这一趋势预计将进一步增强。然而，缺乏标准化且易获取的基准数据集，严重制约了该方向的进展。为此，我们提出一个包含七个基准数据集的集合，专门用于支持RNA三维结构功能预测任务。基于成熟的Python库rnaglib，该工具包实现了数据分发与编码的标准化，提供数据集划分和评估工具，并构建了一个全面、易用的模型对比环境。其模块化与可复现的设计鼓励社区参与，支持快速定制。为验证其有效性，我们采用关系图神经网络对所有任务进行了基线测试。
原文摘要 · Abstract (English)
The relationship between RNA structure and function has recently attracted interest within the deep learning community, a trend expected to intensify as nucleic acid structure models advance. Despite this momentum, the lack of standardized, accessible benchmarks for applying deep learning to RNA 3D structures hinders progress. To this end, we introduce a collection of seven benchmarking datasets specifically designed to support RNA structure-function prediction. Built on top of the established Python package rnaglib, our library streamlines data distribution and encoding, provides tools for dataset splitting and evaluation, and offers a comprehensive, user-friendly environment for model comparison. The modular and reproducible design of our datasets encourages community contributions and enables rapid customization. To demonstrate the utility of our benchmarks, we report baseline results for all tasks using a relational graph neural network.
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